Identification of Metabolites
through GC/LC–MS Processed
Data using Different
Reference Libraries and
Their Comparison
INTRODUCTION
Metabolomics is the field of biology, referring to the scientific study of chem-ical processes including metabolites. Metabolites are smaller-sized molecules of <1500 Da involving peptides, lipids, drugs, chemical compounds, micro-bial plant, environmental and mammalian systems. One of the limiting fac-tors in the metabolomics study is that of chemically identifying metabolites from mass spectrometric (MS) signals, present in complex datasets, therefore, the identification and quantification of metabolites is a challenging task1.
Gas/liquid chromatography–MS (GC/LC–MS) and many computational tools have been developed and applied to support the profiling and analy-sis of metabolomics data. The GC–MS-based metabolomics is a robust tech-nique because of the reproducibility of the chromatographic retention time, which can be paired with the EI-derived fragmentation spectra. Whereas, in the last decades, LC–MS-based analysis has come to the forefront because of its ability to analyse and identify thermally labile metabolites12. Recent years
have seen exponential growth in a number of metabolomics studies and the numbers of spectral libraries have been growing in size, thus enabling ever-increasing number of target metabolites to be identified by techniques2.
The library search is a useful tool for identifying chromatographic peaks of a chromatogram and the database used in the identification of mass spec-tra (MS) is known as MS libraries. The identification of metabolites can be done through libraries and by applying tools which enables data analysis, processing and identification. One of the tools which can be used for this purpose is a web-based XCMS Online tool3 (https://xcmsonline.scripps.
edu/). It is widely used for the comparative analysis and does identification of metabolites, but involves lots of manual processing. Another tool named automated MS deconvolution and identification system (AMDIS)4 is the
Sarika Srivastava1, Priya Ranjan Kumar2*, Santosh Kumar Mishra2
1 Assistant Professor, IMS Ghaziabad (University Courses Campus), NH24, Adhyatmik Nagar, Ghaziabad, UP, India 2 Assistant Professor, IMS Engineering
College, NH24, Adhyatmik Nagar, Ghaziabad, UP, India
Address reprint requests to *Priya Ranjan Kumar, Assistant Professor, IMS Engineering College, NH24, Adhyatmik Nagar, Ghaziabad, UP, India
E-mail: [email protected]
Article citation: Srivastava S, Kumar PR, Mishra SK. Identification of metabolites through GC/LC–MS processed data using different reference libraries and their comparison. J Pharm Biomed Sci
2016;06(06):363–368.
Available at www.jpbms.info
Statement of originality of work: The manuscript has been read and approved by all the authors, the requirements for authorship have been met, and that each author believes that the manuscript represents honest and original work.
Sources of funding: None.
Competing interest / Conflict of interest: The author(s) have no competing interests for financial support, publication of this research, patents and royalties through this collaborative research. All authors were equally involved in discussed research work. There is no financial conflict with the subject matter discussed in the study.
Disclaimer: Any views expressed in this paper are those of the authors and do not reflect the official policy or position of the Department of Defense.
NLM Title J Pharm Biomed Sci
CODEN JPBSCT
2230-7885 ISSN No
ABSTRACT
Much significant advancement has been reported in the last few years in the field of metabolomics studies. The high-end computer applications are already contributing to the research and analysis in the field of life sciences. There are many hardware and softwares available, which can be used with various biomolecular separation and analy-sis instruments like chromatography, mass spectroscopy (MS), NMR, etc. The metabolite identification is the crucial part of the metabolomics study. The biosample collected from any resource need to be analysed from GC/LC–MS or NMR-type instrumentation to pre-cisely identify the compounds present in the sample qualitatively and quantitatively. There are many tools and databases already available which can be used for the pre-processing, processing and analysis of raw data generated from these instruments. Various reference libraries are also available, which can be used for the identification of metabolites pres-ent in the sample after the processing of raw data. In this study, we have reviewed and compared different libraries and tools available for the metabolite identification from GC/ LC–MS data.
KEYWORDS metabolomics, reference libraries, GC–MS, LC–MS, metabolite profiling
most popular software for the identification and quanti-fication of metabolomics data and it is also linked to the National Institute of Standards and Technology(NIST) standard reference database (http://www.nist.gov/srd/ nist1a.cfm) for the identification of various compounds. The unknown spectra can be identified by comparing its measured spectrum with the reference spectra in MS libraries. These software’s have in-built MS libraries which can be used as a reference library for searching the metabolites of sample analysed by the techniques.
The peaks in the spectra represent the particu-lar metabolite related to its retention indices (RI) and intensity with their corresponding m/z values5.
There are few libraries which are freely or commer-cially available such as NIST (version 14.0), Golm metab-olome database (GMD)6, FiehnLib7, human metabolome
library of HMDB (HML)8, MassBank21, etc. Only a small
percentage of all known metabolites are available com-mercially in these MS libraries. Therefore, MS libraries containing data for all metabolites are needed to be con-structed9. Some metabolites from sample spectrum are
not found in reference spectrum; so, to identify those metabolites, their molecular formulae are used to con-struct con-structures and de novo methods are used to iden-tify the metabolites9,10. In this study, we are reviewing
some of the reference libraries which can be used for the metabolite identification from GC/LC–MS data.
DESCRIPTION OF LIBRARIES AND DATABASES FOR METABOLITE IDENTIFICATION
GMD
GMD provides public access to custom MS libraries which are implemented in the MS and RI libraries (MSRI). http://csbdb.mpimp-golm.mpg.de/csbdb/gmd/msri/ gmd_msri.html. These libraries of MSRI can be used with the NIST software to identify metabolites according their spectral tags and RIs by simple search or by specific query tools implemented in MSRI.
MSRI construct non-supervised MSRI database from a plant species. However, it contains 300–500 MS compo-nents from GC/EI–TOF–MS and GC–quad-MS. An MSRI library has 6,205 MS components that were collected to identify model compounds. Currently, GMD has five downloadable libraries according to the technique that is being used for instance, the Q_MSRI and T_MSRI libraries contain MS tags (MSTs), which were either generated on four identically configured quadrupole GC–MS systems (Q_MSRI) or on a single time-of-flight system (T_MSRI)6.
It also provides MSRI compound search tool which allows searching by compound name and provides access to the MS information at GMD.
At present, the Q_MSRI_ID library contains 1,166 identified or annotated MSTs, which represent 574 non-redundant compounds. Of these, 306 compounds are unambiguously identified, while the residual MSTs are annotated with the best MS match from a commercially
available MS collection of NIST. The T_MSRI_ID collection has a similar size, namely 855 MSTs with 229 identifica-tions within a set of 632 non-redundant components11.
METabolite LINk (METLIN)
METLIN is a publicly available web-based database for visualising and analysis of metabolites. METLIN, a data-base that incorporates MS data from multiple sources such as high-accuracy FTMS, tandem MS spectra (MS/ MS) and LC–MS data. It stands as a convenient and com-prehensive package of valuable resources for characteris-ing known and unknown metabolites. It can be accessed at http://metlin.scripps.edu/12–14.
The tandem MS data have obtained on a 6,510 Q-TOF operated in positive and negative electrospray ionisation (ESI) mode on more than 10,000 distinct metabolites using four different collision energies. Each known metabolite is annotated with its chemical formula, ion fragmentation data, spectral peaks, etc. The researchers can also identify structurally similar metabolites by using chemical sub-structure. It also provides a direct link to Kyoto Encyclopaedia of Genes and Genomes (KEGG) and CASS tag number. In this era of metabolomics, the advancement in small-molecule metabolite profiling helps in improvising our understanding of biological processes and the molecular basis of drugs and diseases13.
Fiehn GC/MS metabolomics RTL Library
(FiehnLib)
The Agilent FiehnLib15 is developed to identify
metabo-lites that are commonly found in metabolomic studies. The library has been compiled from commercially avail-able metabolites that are derived from various databases. The main focus is to identify as many metabolites as possible from GC–MS metabolite profiling. It is primar-ily used as an entry tool for metabolite profiling by GC/ MS while it is not meant to comprehensively cover any specific part of known metabolic pathways or any spe-cific organism. The metabolites included are structurally diverse and allows detection by GC/MS16.
The FiehnLib GC/MS libraries are based on a fatty-acid methyl ester RI system. The library has been established by GC/MS based on time-of-flight MS (GC–TOF) and also on quadrupole MS (GC–Quad) using similar, but not identi-cal, chromatographic and MS parameters. The structures of FiehnLib metabolites were obtained in structure data file format (SDF) from the NCBI’s PubChem chemical data-base. The libraries of MS as well as software programmes search those spectra that are compiled to aid metabolite identification through MS matching7.
NIST
and identifying MS. The NIST is integrated with Agilent ChemStation, MassHunter, Thermo Xcalibur and others. Its library is known for its high-quality coverage and accessibility. The library contains several components which comprise of the EI MS library, MS/MS tandem spectral library, GC data library, NIST MS software17.
The EI MS libraryconsists of 276,248 spectra of 242,466 unique compounds,each with name, formula, molecular structure (mol), molecular weight, CAS/ InChIKey, contributor name,list of peaks and synonyms.
The GC librarycontains 387,463 citations of RI and GC methods for 82,868 compounds with structures, covering both polar and non-polar columns.
TheMS/MS tandem spectral library contains 234,284 tandem spectra: 51,216 ion trap spectra (42,126 ions/ 8,171 compounds) and 183,068 qTOF.
Also, the NIST MS software includes NIST MS Search programme, AMDIS programme and MS Interpreter programme18.
AMDIS
4It automatically finds a target compound in a GC–MS data file. The software programme first deconvolutes the GC/MS data file to find all the separate components. Each of these components is then compared against a library of target compounds. AMDIS uses a library of MS with or without RI to identify compounds in the data file. It can be used to build a user-defined library, either from GC/ MS data files or from the data in the NIST MS Database.
As GC–MS method is the method of choice for iden-tifying volatile compounds in complex mixtures, but it can be fail when the acquired spectra are contaminated with extraneous MS peaks which commonly arise from co-eluting compound and ionisation chamber contami-nants. These extraneous peaks can pose a serious prob-lem for automated identification methods where they can cause identifications to be missed by reducing the spec-trum comparison factor below some pre-set identification threshold. In addition, the presence of spurious peaks in a spectrum adds to the risk of making false identifications19.
AMDIS is an integrated set of procedures for first extract-ing pure component spectra and its related information from complex chromatograms, then using this informa-tion to determine whether the component can be identi-fied as one of the compounds represented in a reference library or not. The practical goal is to reduce the efforts involved in identifying compounds by GC/MS. AMDIS works through various analysis steps includes noise anal-ysis, component perception, spectrum deconvulation and compound identification with spectrum comparison4.
XCMS Software
XCMS is an LC–MS-based data analysis freely avail-able approach under an open-source license at http:// metlin.scripps.edu/download/3. It provides metabolite
identification from scratch for instance, preprocessing
of the raw data of an approach, then by the annotation. The preprocessing involves retention time alignment, peak filtration, peak matching, etc.
The simultaneous separation and detection of metab-olites using both LC/GC and MS produce some complex datasets that require significant preprocessing before multiple samples can be analysed statistically. A prepro-cessing routine based on peak detection that requires a robust method for reproducibly characterising peaks in the three-dimensional space (time, mass and intensity) which has been defined by the LC/MS data.
The processing of metabolomic data by XCMS Online is organised in three simple steps: data upload, parameter selection and result interpretation. The raw data files have to be uploaded, followed by the parameter selection. The predefined parameter sets for different instrument setups are available, e.g., HPLC/Q-TOF, UPLC/Q-TOF, HPLC/ Orbitrap, HPLC/single quad MS and GC/single quad MS. After selecting a finished job from the job list, XCMS Online displays various figures that provide an overview of the result. The feature table browser displays detailed information for each individual feature including statistics, extracted ion chromatograms (EIC), MS, isotopes, adducts and putative METLIN IDs. In the detailed view of MetlinID (MID), it provides the knowledge of molecule name, syn-onyms, structure and spectrum (which show fragments of a molecule at a particular retention time) along with it link to CAS and KEGG database3,20.
MassBank
MassBank is the first public repository and first inter-nationally allied MS database of small chemical com-pounds for life sciences (<3000 Da). It is a distributed database in which research group provides data from its own data servers that are distributed on the Internet. Its data are mainly consisting of MS of primary metabolites, flavonoids, gibberellins, saponins, carotenoids, phos-pholipids and oligosaccharides.
MassBank features two tools to search for chemical compounds in its repository, that is, Quick Search and Substructure Search. Quick Search retrieves chemical com-pounds by the chemical name, chemical formula and a list of the m/zand relative intensity values. The search results show the chemical compounds with their chemical names, spectral data and chemical structure. Substructure Search retrieves chemical compounds that contain a specified chemical substructure as a part of their chemical structure.
One of the most important applications of MassBank data in the life sciences is metabolite identification.
Generally, ESI–MS2 data of chemical compounds are
useful as reference data for metabolite identification when the analytical conditions of the query ESI–MS2 data are
the same as or very similar to those of the reference MS. When the query and the reference chemical compounds are the same, the spectral search retrieves the reference
the metabolite identification from MS, they merged ESI– MS2 data of identical chemical compounds which were
analysed under different experimental conditions21.
MMCD
Madison Metabolomics Consortium Database (MMCD) is a database maintained by NMR facility, based on NMR spectroscopy and MS. MMCD serves as a hub for informa-tion on small molecules of biological interest taken from various electronic databases and the scientific literatures. The MMCD search engine supports versatile data mining and allows the users to make individual or bulk queries on the basis of NMR and/or MS data along with other criteria. The database contains information on ~20,306 compounds. It mostly gives emphasis to Arabidopsis thaliana, whereas, it can also apply to other organisms22–23.
IDEOM
IDEOM is a user-friendly application for the analysis of LCMS metabolomics data. The main functions of IDEOM are noise filtering, metabolite identification and visualisa-tion of results. IDEOM is a Microsoft excel template with a collection of VBA macros that enable automated data pro-cessing of high-resolution LC–MS data from untargeted metabolomics. Its GUI allows data-processing methods such as mzMatch from within excel. The raw LC–MS data files are processed through IDEOM using XCMS and mzMatch tools. The pre-processed peak lists from mzMatch can be directly imported into IDEOM as text or csv files. The automated pre-processing steps in IDEOM reduce the need for manual curation of LC–MS data by applying filters to remove hundreds of false identifications. Metabolites can be identified by matching the accurate mass and retention time of observed peaks of metabo-lites in the database, which incorporates all likely metab-olites from a wide range of biological databases. IDEOM tool also includes merging dual-polarity data, chemical formula determination, stable isotope tracking and tar-geted analysis. The IDEOM templates are freely available at http://mzmatch.sourceforge.net/ideom.html24.
mzCloud
mzCloud is a MS(n) library. It is based on the spectral tree similarity, an algorithm called precursor ion fingerprint-ing (PIF) which enables the identification of compounds even if they are not in the library, and the fragmental peaks can be annotated. PIF is a mode of identifying the structure of ions from their tandem product spectra to elucidate the chemical structure of an unknown metabolite25,26.
MMD
The Manchester Metabolomics Database (MMD) has been constructed to provide identification of metabolite- specific MS libraries for UPLC–MS and GC–MS data from
genome scale reconstruction, HMDB8, KEGG27, LipidMaps,
DrugBank and BioCyc to provide knowledge on 42,687 exogenous and endogenous metabolite species. The MMD data are available at http://dbkgroup.org/MMD/.
There are two types of identification are achievable in MMD i.e., putative or preliminary identification and definitive identification. Usually in putative identifi-cation, employs one or more molecular properties for identification but does not compare these to the same properties of authentic standard, whereas, it is per-formed for definitive identification.
They applied two iterative processes, where possible, to provide the identification of a wide range of metabo-lites when applying UPLC/GC–MS analytical platforms. Following processing of the raw data through XCMS and univariate/multivariate analysis to identify peaks of interest, applied in two step processes: In the first step, the peaks were first matched for putative or prelimi-nary identification which had 4,915 unique metabolites and in other step, it is matched with the experimen-tally derived MS libraries for definitive identification of metabolites. The derived library has been constructed (which apply chromatography properties, RI and MS) using metabolites that were commercially available and purchased as authentic standards from Sigma-Aldrich, the supplier. 1,068 metabolites were found to be com-mercially available and many metabolites were also obtained from the linked databases28.
PUTMEDID_LCMS
PUTMEDID_LCMS is the collection of workflows that have been developed for the rapid, automated and high-throughput annotation and putative metabolite identification of LC–MS and UPLC–MS-derived metabolic datasets in a freely available package. The workflows were developed in the Taverna workflow management system. However, the three workflows are: workflow for correla-tion analysis, workflow for metabolic feature annotacorrela-tion and workflow for metabolite annotation. These work-flows reduce the number of false positives by eliminating the inaccurate matching of many artefacts, isotope and adducts peak. The approach is flexible and it is indepen-dent of the chromatographic deconvolution method and the analytical instrument applied to it. Additional adducts can be added to the adduct file for user-specific instru-ments, the data and organism-specific metabolite refer-ence files can be used. An assessment of the workflows has been made using two reference files: (i) a listing of unique accurate mass/MF data from PubChem using specific elements only, and (ii) The MMD data28,29.
ALLocator
peak detection, spectra deconvulation, compound iden-tification and finally the data exploration and annotation. It has new processing pipeline for spectra deconvulation, ‘ALLocatorSD’. The ALLocator web interface provides sev-eral interactive and dynamic views to explore and edit the results generated by the ALLocatorSD-processing pipeline (or by CAMERA30) for the automatic assembly of
pseudo-spectra. The integration of the platform has been done with public metabolite and MS databases (KEGG, ChemSpider, MassBank and new powerful tools for instance, the spec-trum-aware mass decomposition). It is freely available at https://allocator.cebitec.uni-bielefeld.de/landing.htm31.
CONCLUSION
The annotation of metabolites is a challenging task as the number of known standard metabolites are limited and the chances of being correctly examined is less; however, the false positive values may occur. Some soft-ware has their own reference libraries but they have con-strains on number of metabolites. There are few other libraries which are freely or commercially available such as NIST (version 14.0), GMD, FiehnLib, HML, MassBank, etc. Hence, these reference libraries can be used for the further identification of different metabolites from GC/ LC–MS raw data collected in the laboratory.
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Table 1 Comparison table of different tools and databases which can be used for metabolite identification.
Sl. no. Tools Species Techniques Free/paid Type No. of metabolites Type of data
1 GMD (MSRI Library) Plants GC/EI–TOF–MS, GC–quad-MS Free Library 6205 Processed
2 METLIN Plants/animals FTMS, MS/MS, LC–MS Free Library 240572 Processed
3 mzCloud Plants/animals MS/MS or MS (n) Free Library >2800 Processed
4 FiehnLib Plants/animals GC–MS Paid Library 2,212 EI-spectra over 1,000 metabolites Processed
5 NIST/EPA/NIH Plants/animals EI–MS, MS–MS, GC–MS Paid Library
276248-EI–MS library 387463-GC–MS library 234284-MS–MS spectral library
Processed
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9 XCMS Plants/animals LC/GC–MS Free Software 240572-uses metlin library Raw
10 IDEOM Plants/animals LC–MS Free Software Raw
11 MetaboAnalyst Plants/animals GC/LC–MS, NMR/MS Free Software Raw
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